Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Иерархическая линейная модель (HLM)× | Многоуровневое моделирование× | |
|---|---|---|
| Область≠ | Статистика | Статистика исследований |
| Семейство≠ | Regression model | Process / pipeline |
| Год появления | 1992 | 1992 |
| Автор метода≠ | Bryk & Raudenbush | Anthony Bryk and Stephen Raudenbush |
| Тип≠ | Multilevel linear regression | Method |
| Основополагающий источник≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN: 978-0761919049 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Другие названия | HLM, multilevel linear model, nested data model, random coefficient model | HLM, mixed-effects models, random effects models, MLM |
| Связанные≠ | 4 | 3 |
| Сводка≠ | The Hierarchical Linear Model (HLM) is a multilevel regression method designed for data in which lower-level units (e.g., students, patients) are nested within higher-level groups (e.g., schools, hospitals). It simultaneously models within-group relationships and between-group variation, producing unbiased estimates and correct standard errors that ordinary regression cannot provide for nested data. | Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies. |
| ScholarGateНабор данных ↗ |
|
|